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Related Concept Videos

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Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Conservation of Protein Domains Over Different Proteins

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

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Published on: July 25, 2013

Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection.

Marc N Offman1, Alexander L Tournier, Paul A Bates

  • 1Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, Lincoln's Inn Fields Laboratories, London, WC2A 3PX, UK. marc.offman@cancer.org.uk

BMC Structural Biology
|August 5, 2008
PubMed
Summary
This summary is machine-generated.

A new strategy using alternating evolutionary pressure (AEP) in genetic algorithms (GA) enhances protein modelling. This method improves the selection of accurate protein models by overcoming energy barriers, boosting success rates from 25% to 40%.

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein modelling

Background:

  • Automatic protein modelling pipelines are advancing but face challenges in template selection, refinement, and model selection.
  • The complexity of these pipelines necessitates continuous improvement for greater accuracy.

Purpose of the Study:

  • To analyze individual components of automatic protein modelling pipelines.
  • To explore a novel strategy for protein conformation sampling using Genetic Algorithms (GA).

Main Methods:

  • Analysis of each step within an automatic modelling pipeline.
  • Implementation of alternating evolutionary pressure (AEP) within GA runs.
  • AEP allows unrestrained, linear growth of model populations in intermediate rounds.

Main Results:

  • Identified non-intuitive trends in protein modelling pipeline steps.
  • The AEP strategy demonstrated improved performance in overcoming local energy barriers.
  • AEP successfully selected the best models for 40% of targets.

Conclusions:

  • Alternating evolutionary pressure (AEP) enhances the overall performance of Genetic Algorithms (GA) in protein modelling.
  • The AEP approach significantly increases the rate of successful model selection compared to standard GA.
  • This strategy offers a promising avenue for improving automated protein structure prediction.